In the development of computer vision algorithms through supervised machine learning to detect objects in a driving environment, diverse sets of sensor data are needed to train, develop, test and prove the detection algorithms and additional downstream functions associated with the algorithms. However, it usually takes considerable money, time and resources to acquire real-world sensor data. For example, to obtain real-world sensor data, sensors may need to be physically disposed along a driving path or mounted on a vehicle, and actual driving runs may need to be performed on various types of roads, for various traffic conditions and under various weather and lighting conditions in order for the sensors to collect numerous sets of sensor data for a variety of scenarios. For instance, for the robustness of the algorithms under various conditions, ambient variables or parameters such as weather, temperature, wind, lighting conditions and other factors may need to be included in the real-world sensor datasets. Consequently, the number of sets of sensor data to be collected may be enormous. In general, sensor data collected from hundreds or even thousands of miles of road, which may include as many as thousands of diverse images, is needed to develop an accurate and reliable computer vision detection algorithm, which translates to considerable amount of time, money and resources required to acquire such data.